EVOLUTIONARY ALGORITHM USED IN EFFICIENT CONGESTION MANAGEMENT ANALYSIS

International Journal of Research in Advanced Technology - IJORAT Vol. 2, Issue 3, MARCH 2016

All Rights Reserved © 2016 IJORAT 1

EVOLUTIONARY ALGORITHM USED

IN EFFICIENT CONGESTION

MANAGEMENT ANALYSISA.Anish Joy

1, Dr. .P.Annapandi,M.E.,PhD

2,S.Jeya Pradeepa

3

1PG student, Dept of EEE, FRANCIS XAVIER Engineering College, Tamilnadu, India

2Professor, Dept of EEE,FRANCIS XAVIER Engineering College, Tamilnadu, India

3PG student, Dept of EEE, FRANCIS XAVIER Engineering College, Tamilnadu, India

Abstract: A particle swarm optimization (PSO) technique for a reactive power wind farm (WF) dispatch

function, in order to calculate the reactive power reference for each wind turbine (WT). The dispatch can

be formulated as the problem of minimize the difference in power interchange at interconnection point

(PCC). Incorporation of PSO as a optimization technique for the WF dispatch make possible consider

different parameters to improve it performance and give it more capabilities. a comprehensive analysis of

the dynamic interactions between wind energy curtailment and an energy storage system (ESS) when the

ramping rates of power plants are considered. An analytical framework is developed to study different

mitigation measures in terms of total energy curtailed, total congestion costs, line load factor and

congestion probability

Keywords Particle swarm optimization (PSO), Energy storage system (ESS), Genetic algorithm (GA),

Induction Generator (IG)

I INTRODUCTION

Every day it is more recognized the large potential

of renewable energies to displace greenhouse gases

emissions and to achieve climate change mitigation

targets. Among these technologies, wind power has

been the fastest growing renewable energy

worldwide. However, there are many integration

challenges regarding the impact of wind energy in

both the design and operation of power systems.

Thus, according, a cost effective transition to a

system with high levels of penetration of

renewable, will need not only improvements in the

electricity infrastructure but also fundamental

changes in the philosophy of network operation and

development. Special attention has to be devoted to

the transmission system as new wind capacity

deployment may also introduce bottlenecks in the

grid. In this context, one of the most important

aspects of the future integration of renewable

energies is the reduction of transmission congestion

while maintaining minimum impact on the

reliability of the grid and the capital and

operational costs of the system

In general, congestion management approaches can

be classified into systemic and local solutions.

Systemic solutions involve a system-level

minimization of the total operational costs, while

fulfilling the network security constraints. The

most common strategy for congestion management

is to compensate the fluctuation of the wind energy

through a re-dispatch of other power plants. This

approach has the disadvantage that deviates from

the economic optimality, and the accuracy of the

solution is directly affected by the forecasting

errors in both wind generation and load. In a

comprehensive review of different approaches for

congestion management in competitive markets is

presented. In a real time congestion supervisor is

proposed in order to reduce the re-dispatching. The

deployment of this technique requires the

installation of network controllers located in the

transmission lines and in each generator. Besides

the upgrade of the existing communication

network, this congestion management approach

would need the modification of the current grid

codes as well.

II CONGESTION MANAGEMENT

Congestion management in a multi-buyer/

multi-seller system is one of the most involved

tasks if it has to have a market based solution with

economic efficiency. In a vertically integrated

utility structure, activities such as generation,

transmission and distribution are within direct

control of a central agency or a single utility.

Generation is dispatched in order to achieve the

system least cost operation. Along with this, the

optimal dispatch solution using security

constrained economic dispatch eliminates the

possible occurrence of congestion. This effectively

means that generations are dispatched such that the

power flow limits on the transmission lines are not

exceeded.



According to reference the congestion management

is defined as “the comprehensive set of actions or

procedures to ensure that no violations of the grid

constraints occur”. By following this approach, this

work proposes a comprehensive methodology to

study the dynamic interactions of wind curtailment

and energy storage for transmission congestion

management while considering ramp-up and ramp-

down rates of generating units. The methodology is

applied to a real network system located in the

northern part of Chile.

III ENERGY STORAGE MODEL

There are different ESS technologies that can be

used for congestion mitigation. Due to their current

stage of development at a high rated capacity (100

MW), four technologies are the most suitable for

congestion management: pumped hydro system

(PHS), compressed air energy storage (CAES),

thermal energy storage (TES), and battery energy

storage system (BESS). A generic model of an ESS

that considers operational rules to deal with the

dynamic congestion management is proposed. For

simplicity, we assume that the ESS and the wind

farm are connected to the same busbar. Note

however that, in the general case, not every wind

farm busbar holds a connection to an ESS.The final

application of the proposed framework is

described. The ESS stores energy from the wind

farm when there is overload in transmission

capacity and supplies power back to the grid when

the transmission congestion is relieved. This

behaviour translates into a simple control strategy,

where other factors like changes in the energy

price, operational reserves opportunities, etc., are

not considered. Fig.3.1 shows the diagram of the

ESS model based on reference. Figure shows the

ESS model used for operational analysis.

Fig 3.1-ESS model used for operational analysis.

IV PARTICLE SWARM OPTIMIZATION

(PSO)

Particle Swarm Optimization Swarm

Intelligence (SI) is an innovative distributed

intelligent paradigm for solving optimization

problems. PSO incorporates swarming behaviours

observed in flocks of birds, schools of fish, or

swarms of bees, and even human social behaviour,

from which the idea is emerged. PSO is a

population-based optimization tool, which could be

implemented and applied easily to solve various

function optimization problems. As an algorithm,

the main strength of PSO is its fast convergence,

which compares favourably with many global

optimization algorithms like Genetic Algorithms

(GA) Simulated Annealing (SA) and other global

optimization algorithms.

Particle swarm optimisation (PSO) is an

evolutionary computation technique that applies an

analogy of swarm behaviour of natural creatures. It

has been motivated by the behaviour of organisms

acting as a unit, for example he schooling of

shocking of birds. Birds usually seek food (their

objective) in swarms. Each individual bird (agent)

reconfigures its behaviour, based on its own

experience and the experiences of others.

Minimize(x; y)

Where x denotes the dependent variables,

consisting in bus voltages, transmission line

loadings, etc. and where y denotes the independent

variables, in this case WT reactive power

consumption/generation. Basically, the position of

each agent has an associated velocity vki , this is

responsible for the movement and theposition

change of the agents. Each agent knows its best

historical value and the corresponding position. In

addition, each agent is aware of the value and

corresponding position of the best agent of the

swarm.

A ALGORITHM

In a PSO algorithm, the population has n

particles that represent candidate solutions. Each

particle is a k-dimensional real-valued vector,

where k is the number of the optimized parameters.

Therefore, each optimized parameter represents a

dimension of the problem space. The modified PSO

technique for integer problem can be described in

the following steps

Step 1: (Initialization):

Set t=0 and generate random n particles,

{Xi (0), i=1,2,..n}. Each article is considered to be

solution for the problem and it can be described as

Xi (0)=[ xi,1(0); xi ,2(0); ……;xi ,m(0)]

Each control variable will have a range [xmin,

xmax]. Each particle in the initial population is



evaluated using the objective function f. For each

particle, set

Xi*(0) =Xi(0) and

Fi* = fi ; i=1,2,3.....,n.

Search for the best value of the objective function

fbest .Set the particle associated with fbest as the

global best,X**

(0),with an objective function. Set

the initial value of the inertia weight w(0).In this

study the objective function is the optimal power

flow ,which will be calculated after running the

power flow and meeting all our constraints.

Step 2: Counter Updating:

Update the counter t= t +1

Step 3: Velocity updating:

Using the global best and individual best,

the ith

particle velocity in the kth

dimension in this

study (integer problem) is updated according to the

following equation:

Vi,k(t) = w(t).vi,k(t-1) + b1s1(xi*,k(t-1)-xi,k(t-1))

+b2s2(xi**,k(t-1) – xi,k (t-1)

From the previous equation i is the particle number,

b1, b2 are positive constants, s1 s2 are uniformly

distributed Random numbers in [0, 1] and k is the

kth

control variable. Then, check the velocity limits.

If the velocity violated its limit, set it at its proper

limit. The second term of the above equation

represents the cognitive part of the PSO where the

particle changes its velocity based on its own

thinking and memory. The third term represents the

social part of PSO where the particle changes its

velocity based on the social-psychological

adaptation of knowledge.

Step 4: Position updating:

Based on the updated velocity, each

particle changes its position according to the

following equation:

Xi,k(t) = xi,k(t-1) + vi,k(t)

Step 5: Individual best updating:

Each particle is evaluated and updated

according to the update position.

Step 6:

Search for the minimum value in the

individual best and its solution has ever been

reached so far, and considers it to be the minimum.

Step 7:

Stopping criteria: if one of the stopping criteria is

satisfied, then stop otherwise go to step-2.

Fig 4.1 Flowchart for PSO Algorithm

V PROPOSED MODEL

In this proposed system, we present a

comprehensive analysis of the dynamic interactions

between wind energy curtailment and an energy

storage system (ESS) when the ramping rates of

power plants are considered. An analytical

framework is developed to study different

mitigation measures in terms of total energy

curtailed, total congestion costs, line load factor

and congestion probability. This framework is

tested in a real case study and a sensitivity analysis

Initialization

Power

Flow

Constraine

d Satisfied

Update Counter

Update Velocity

Update Position

Update individual Best , If

Needed

Update Global Best , If Needed

Power Flow

Constraint

Stop

NO

YES

YES

NO



is performed to identify the influence of the main ESS design parameters in congestion

Fig-5.1 Simulation Model Of Proposed System

Figure 5.1 shows the simulation model of

the power system. In this system the generation and

distribution side was presented in this model. The

asynchronous generator was used in the wind

turbine. The main component of the generation side

is wind generator. The output of this model is given

below. The output is taken in the wind generator

side and also load side which shows the congestion

occur in the transmission side was mitigate and that

was managed with the PSO algorithm

Figure 5.2 shows the model of the

induction generator. In this project the IG used for

the better performance. It has the small settling

time to reach the constant rotor speed.

The output of the induction generator and

the system model output is given in the figure 5.3

and the figure 5.4



Fig5.2 Model of induction Generator

Fig 5.3 Output of Wind generator

Fig 5.4 Output of Voltage level in the load



VI CONCLUSION

In this work, wind power curtailment and energy storage

as transmission congestions mitigation measures are

analyzed. It is found that there is a dynamic interaction

that introduces an over cost when slow power plants are

re-dispatched. Congestion mitigation measures are

compared in terms of congestion probability, line load

factor and total energy curtailed. The following

behaviour is observed:

When using wind curtailment, there are two

effects. There is an economic impact on the

wind generator, due to the energy curtailed, not

sold to the system. Also, there is an over cost

on the system due to the re-dispatch.

When using ESS in combination with wind curtailment

the over cost effect may be reduced, but it cannot be

eliminated. As in the previous case, there is an economic

impact on the wind generator, and also on the system

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EVOLUTIONARY ALGORITHM USED IN EFFICIENT CONGESTION MANAGEMENT ANALYSIS

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